David Williamson Shaffer (born May 10, 1964 in New York City, NY) is the Vilas Distinguished Achievement Professor of Learning Science at the University of Wisconsin–Madison in the department of Educational Psychology, the Obel Foundation Professor of Learning Analytics at Aalborg University in Copenhagen, a Data Philosopher at the Wisconsin Center for Education Research, and Principal of EFGames, LLC.

Shaffer was a Marie Curie Fellow at Utrecht University from 2008–2009, and received a Fellowship to the European Institute for Advanced Study at École normale supérieure de Lyon for 2015.

[6][7] ENA is an example of quantitative ethnography, a term coined by Shaffer to describe a set of research methods leveraging novel statistical models and data visualization techniques to augment the scale and power of rich qualitative analyses.

[8] Originally developed to identify meaningful differences between groups of learners,[9] quantitative ethnographic approaches have also been used to analyze data from other disciplines, such as history and systems engineering.

[7] Players become high-powered negotiators, deciding the fate of a real medical controversy: the ethics of transplanting organs from animals into humans.

They work together to tackle the urban issues that face their city, using iPlan, a Geographic Information System (GIS) tool that helps them develop a comprehensive plan for their community.

Players are welcomed as early career hires into the fictitious company Nephrotex, whose core technology is the ultrafiltration unit, or dialyzer, of a hemodialysis machine.

Although originally designed to assess epistemic frames in virtual game environments, ENA is used more generally to quantify the structure of connections that constitute complex thinking as they manifest in discourse, or more generally to quantify and visualize the development of any phenomenon, such as fMRI data on brain activity, that can be characterized by changes in connections over time.

For any two elements of the target domain, the strength of their association in an epistemic network is computed based on the frequency of their co-occurrence the logfile data.